Configurable keypoint descriptor generation

ABSTRACT

Embodiments relate to generating keypoint descriptors of the keypoints. An apparatus includes a pyramid image generator circuit and a keypoint descriptor generator circuit. The pyramid image generator circuit generates an image pyramid from an input image. The keypoint descriptor generator circuit determines intensity values of sample points in the pyramid images for a keypoint and determines comparison results of comparisons between the intensity values of pairs of the sample points. The keypoint descriptor generator circuit generate bit values defining the comparison results for the keypoint, each bit value corresponding with one of the comparison results, and generate a sequence of the bit values defining an ordering of the comparison results based on importance levels of the comparisons, where the importance level of each comparison defines how much the comparison is representative of features. Bit values for comparisons having the lowest importance levels may be excluded from the sequence.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a continuation of U.S. patent applicationSer. No. 17/206,901 filed on Mar. 19, 2021, which is hereby incorporatedby reference in its entirety.

BACKGROUND 1. Field of the Disclosure

The present disclosure relates to a circuit for processing images andmore specifically to identifying and extracting points of interest inimages.

2. Description of the Related Arts

Image data captured by an image sensor or received from other datasources is often processed in an image processing pipeline beforefurther processing or consumption. For example, raw image data may becorrected, filtered, or otherwise modified before being provided tosubsequent components such as a video encoder. To perform corrections orenhancements for captured image data, various components, unit stages ormodules may be employed.

Such an image processing pipeline may be structured so that correctionsor enhancements to the captured image data can be performed in anexpedient way without consuming other system resources. Although manyimage processing algorithms may be performed by executing softwareprograms on central processing unit (CPU), execution of such programs onthe CPU would consume significant bandwidth of the CPU and otherperipheral resources as well as increase power consumption. Hence, imageprocessing pipelines are implemented as a hardware component separatefrom the CPU and dedicated to perform one or more image processingalgorithms.

SUMMARY

Embodiments relate to extracting features from images, such as byidentifying keypoints and generating keypoint descriptors of thekeypoints. Some embodiments include an apparatus including a pyramidimage generator circuit and a keypoint descriptor generator circuit. Thepyramid image generator circuit generates an image pyramid from an inputimage. The image pyramid includes pyramid images at different octavesand scales. The keypoint descriptor generator circuit determinesintensity values of sample points in the pyramid images for a keypointand determines comparison results of comparisons between the intensityvalues of pairs of the sample points. The keypoint descriptor generatorcircuit generates bit values defining the comparison results for thekeypoint. Each bit value corresponds with one of the comparison results.The keypoint descriptor generator circuit generates a sequence of thebit values defining an ordering of the comparison results based onimportance levels of the comparison. The importance level of eachcomparison defines how much the comparison is representative offeatures.

Some embodiments include a method for generating keypoint descriptors ofkeypoints. A pyramid image generator circuit generates an image pyramidfrom an input image. The image pyramid includes pyramid images atdifferent octaves and scales. A keypoint descriptor generator circuitdetermines intensity values of sample points in the pyramid images for akeypoint and determines comparison results of comparisons between theintensity values of pairs of the sample points. The keypoint descriptorgenerator circuit generates bit values defining the comparison resultsfor the keypoint. Each bit value corresponds with one of the comparisonresults. The keypoint descriptor generator circuit generates a sequenceof the bit values defining an ordering of the comparison results basedon importance levels of the comparison. The importance level of eachcomparison defines how much the comparison is representative offeatures.

Some embodiments include a system including an image sensor and an imagesignal processor coupled to the image sensor. The image signal processorincludes a pyramid image generator circuit and a keypoint descriptorgenerator circuit. The pyramid image generator circuit generates animage pyramid from the input image. The image pyramid includes pyramidimages at different octaves and scales. They keypoint descriptorgenerator circuit determines intensity values of sample points in thepyramid images for a keypoint and determines comparison results ofcomparisons between the intensity values of pairs of the sample points.The keypoint descriptor generator circuit generates bit values definingthe comparison results for the keypoint. Each bit value corresponds withone of the comparison results. The keypoint descriptor generator circuitgenerates a sequence of the bit values defining an ordering of thecomparison results based on importance levels of the comparison. Theimportance level of each comparison defines how much the comparison isrepresentative of features.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level diagram of an electronic device, according to oneembodiment

FIG. 2 is a block diagram illustrating components in the electronicdevice, according to one embodiment.

FIG. 3 is a block diagram illustrating image processing pipelinesimplemented using an image signal processor, according to oneembodiment.

FIG. 4 is a block diagram illustrating a portion of the image processingpipeline including circuitry for feature extraction, according to oneembodiment.

FIG. 5 is a block diagram of a feature extractor circuit, according toone embodiment.

FIG. 6 is a block diagram of a pyramid image generator, according to oneembodiment.

FIG. 7 is a block diagram of a response map (RM) generator and intraoctave keypoint candidate selector for a single octave, according to oneembodiment.

FIG. 8 is a block diagram of an inter octave keypoint selector,according to one embodiment.

FIG. 9 is a block diagram of a keypoint descriptor generator, accordingto one embodiment.

FIG. 10 shows an even sample pattern and an odd sample pattern,according to one embodiment.

FIG. 11 is a flowchart illustrating a method for keypoint descriptorgeneration, according to one embodiment.

The figures depict, and the detail description describes, variousnon-limiting embodiments for purposes of illustration only.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,the described embodiments may be practiced without these specificdetails. In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

Embodiments of the present disclosure relate to circuitry for generatingkeypoint descriptors of keypoints in an image pyramid. Each keypointdescriptor for a keypoint includes a sequence of bit values definingcomparison results of comparisons between intensity values of pairs ofsample points of the keypoint. The sequence of bit values defines anordering of the comparison results based on importance levels of thecomparisons. The importance level of each comparison defines how muchthe comparison is representative of features. The sequence of bit valuesmay be ordered with the less significant bits corresponding with higherimportance levels of the comparisons and more significant bitscorresponding with lower importance levels of the comparisons. Inanother example, less significant bits in the sequence of bit valuescorrespond with lower importance levels of the comparisons and moresignificant bits in the sequence of bit values correspond with higherimportance levels of the comparisons. The bits corresponding with lessimportant comparisons may be removed or excluded from the sequence ofbit values to reduce the data size of the keypoint descriptor.

By ordering the sequence of bit values based on the importance level ofthe comparisons, one or more bits corresponding with the least importantcomparisons may be removed from one end of the sequence of bit values.The remaining bit values represent the most important comparisons, thusincreasing the effectiveness of the keypoint descriptor in terms ofbeing representative of features for variable bit lengths.

Exemplary Electronic Device

Embodiments of electronic devices, user interfaces for such devices, andassociated processes for using such devices are described. In someembodiments, the device is a portable communications device, such as amobile telephone, that also contains other functions, such as personaldigital assistant (PDA) and/or music player functions. Exemplaryembodiments of portable multifunction devices include, withoutlimitation, the iPhone®, iPod Touch®, Apple Watch®, and iPad® devicesfrom Apple Inc. of Cupertino, Calif. Other portable electronic devices,such as wearables, laptops or tablet computers, are optionally used. Insome embodiments, the device is not a portable communications device,but is a desktop computer or other computing device that is not designedfor portable use. In some embodiments, the disclosed electronic devicemay include a touch sensitive surface (e.g., a touch screen displayand/or a touch pad). An example electronic device described below inconjunction with FIG. 1 (e.g., device 100) may include a touch-sensitivesurface for receiving user input. The electronic device may also includeone or more other physical user-interface devices, such as a physicalkeyboard, a mouse and/or a joystick.

FIG. 1 is a high-level diagram of an electronic device 100, according toone embodiment. Device 100 may include one or more physical buttons,such as a “home” or menu button 104. Menu button 104 is, for example,used to navigate to any application in a set of applications that areexecuted on device 100. In some embodiments, menu button 104 includes afingerprint sensor that identifies a fingerprint on menu button 104. Thefingerprint sensor may be used to determine whether a finger on menubutton 104 has a fingerprint that matches a fingerprint stored forunlocking device 100. Alternatively, in some embodiments, menu button104 is implemented as a soft key in a graphical user interface (GUI)displayed on a touch screen.

In some embodiments, device 100 includes touch screen 150, menu button104, push button 106 for powering the device on/off and locking thedevice, volume adjustment buttons 108, Subscriber Identity Module (SIM)card slot 110, head set jack 112, and docking/charging external port124. Push button 106 may be used to turn the power on/off on the deviceby depressing the button and holding the button in the depressed statefor a predefined time interval; to lock the device by depressing thebutton and releasing the button before the predefined time interval haselapsed; and/or to unlock the device or initiate an unlock process. Inan alternative embodiment, device 100 also accepts verbal input foractivation or deactivation of some functions through microphone 113. Thedevice 100 includes various components including, but not limited to, amemory (which may include one or more computer readable storagemediums), a memory controller, one or more central processing units(CPUs), a peripherals interface, an RF circuitry, an audio circuitry,speaker 111, microphone 113, input/output (I/O) subsystem, and otherinput or control devices. Device 100 may include one or more imagesensors 164, one or more proximity sensors 166, and one or moreaccelerometers 168. Device 100 may include more than one type of imagesensors 164. Each type may include more than one image sensor 164. Forexample, one type of image sensors 164 may be cameras and another typeof image sensors 164 may be infrared sensors that may be used for facerecognition. In addition or alternatively, the image sensors 164 may beassociated with different lens configuration. For example, device 100may include rear image sensors, one with a wide-angle lens and anotherwith as a telephoto lens. The device 100 may include components notshown in FIG. 1 such as an ambient light sensor, a dot projector and aflood illuminator.

Device 100 is only one example of an electronic device, and device 100may have more or fewer components than listed above, some of which maybe combined into a component or have a different configuration orarrangement. The various components of device 100 listed above areembodied in hardware, software, firmware or a combination thereof,including one or more signal processing and/or application specificintegrated circuits (ASICs). While the components in FIG. 1 are shown asgenerally located on the same side as the touch screen 150, one or morecomponents may also be located on an opposite side of device 100. Forexample, the front side of device 100 may include an infrared imagesensor 164 for face recognition and another image sensor 164 as thefront camera of device 100. The back side of device 100 may also includeadditional two image sensors 164 as the rear cameras of device 100.

FIG. 2 is a block diagram illustrating components in device 100,according to one embodiment. Device 100 may perform various operationsincluding image processing. For this and other purposes, the device 100may include, among other components, image sensor 202, system-on-a chip(SOC) component 204, system memory 230, persistent storage (e.g., flashmemory) 228, orientation sensor 234, and display 216. The components asillustrated in FIG. 2 are merely illustrative. For example, device 100may include other components (such as speaker or microphone) that arenot illustrated in FIG. 2 . Further, some components (such asorientation sensor 234) may be omitted from device 100.

Image sensors 202 are components for capturing image data. Each of theimage sensors 202 may be embodied, for example, as a complementarymetal-oxide-semiconductor (CMOS) active-pixel sensor, a camera, videocamera, or other devices. Image sensors 202 generate raw image data thatis sent to SOC component 204 for further processing. In someembodiments, the image data processed by SOC component 204 is displayedon display 216, stored in system memory 230, persistent storage 228 orsent to a remote computing device via network connection. The raw imagedata generated by image sensors 202 may be in a Bayer color filter array(CFA) pattern (hereinafter also referred to as “Bayer pattern”). Animage sensor 202 may also include optical and mechanical components thatassist image sensing components (e.g., pixels) to capture images. Theoptical and mechanical components may include an aperture, a lenssystem, and an actuator that controls the lens position of the imagesensor 202.

Motion sensor 234 is a component or a set of components for sensingmotion of device 100. Motion sensor 234 may generate sensor signalsindicative of orientation and/or acceleration of device 100. The sensorsignals are sent to SOC component 204 for various operations such asturning on device 100 or rotating images displayed on display 216.

Display 216 is a component for displaying images as generated by SOCcomponent 204. Display 216 may include, for example, liquid crystaldisplay (LCD) device or an organic light emitting diode (OLED) device.Based on data received from SOC component 204, display 116 may displayvarious images, such as menus, selected operating parameters, imagescaptured by image sensor 202 and processed by SOC component 204, and/orother information received from a user interface of device 100 (notshown).

System memory 230 is a component for storing instructions for executionby SOC component 204 and for storing data processed by SOC component204. System memory 230 may be embodied as any type of memory including,for example, dynamic random access memory (DRAM), synchronous DRAM(SDRAM), double data rate (DDR, DDR2, DDR3, etc.) RAMBUS DRAM (RDRAM),static RAM (SRAM) or a combination thereof. In some embodiments, systemmemory 230 may store pixel data or other image data or statistics invarious formats.

Persistent storage 228 is a component for storing data in a non-volatilemanner. Persistent storage 228 retains data even when power is notavailable. Persistent storage 228 may be embodied as read-only memory(ROM), flash memory or other non-volatile random access memory devices.

SOC component 204 is embodied as one or more integrated circuit (IC)chip and performs various data processing processes. SOC component 204may include, among other subcomponents, image signal processor (ISP)206, a central processor unit (CPU) 208, a network interface 210, motionsensor interface 212, display controller 214, graphics processor (GPU)220, memory controller 222, video encoder 224, storage controller 226,and various other input/output (I/O) interfaces 218, and bus 232connecting these subcomponents. SOC component 204 may include more orfewer subcomponents than those shown in FIG. 2 .

ISP 206 is hardware that performs various stages of an image processingpipeline. In some embodiments, ISP 206 may receive raw image data fromimage sensor 202, and process the raw image data into a form that isusable by other subcomponents of SOC component 204 or components ofdevice 100. ISP 206 may perform various image-manipulation operationssuch as image translation operations, horizontal and vertical scaling,color space conversion and/or image stabilization transformations, asdescribed below in detail with reference to FIG. 3 .

CPU 208 may be embodied using any suitable instruction set architecture,and may be configured to execute instructions defined in thatinstruction set architecture. CPU 208 may be general-purpose or embeddedprocessors using any of a variety of instruction set architectures(ISAs), such as the x86, PowerPC, SPARC, RISC, ARM or MIPS ISAs, or anyother suitable ISA. Although a single CPU is illustrated in FIG. 2 , SOCcomponent 204 may include multiple CPUs. In multiprocessor systems, eachof the CPUs may commonly, but not necessarily, implement the same ISA.

Graphics processing unit (GPU) 220 is graphics processing circuitry forperforming graphical data. For example, GPU 220 may render objects to bedisplayed into a frame buffer (e.g., one that includes pixel data for anentire frame). GPU 220 may include one or more graphics processors thatmay execute graphics software to perform a part or all of the graphicsoperation, or hardware acceleration of certain graphics operations.

I/O interfaces 218 are hardware, software, firmware or combinationsthereof for interfacing with various input/output components in device100. I/O components may include devices such as keypads, buttons, audiodevices, and sensors such as a global positioning system. I/O interfaces218 process data for sending data to such I/O components or process datareceived from such I/O components.

Network interface 210 is a subcomponent that enables data to beexchanged between devices 100 and other devices via one or more networks(e.g., carrier or agent devices). For example, video or other image datamay be received from other devices via network interface 210 and bestored in system memory 230 for subsequent processing (e.g., via aback-end interface to image signal processor 206, such as discussedbelow in FIG. 3 ) and display. The networks may include, but are notlimited to, Local Area Networks (LANs) (e.g., an Ethernet or corporatenetwork) and Wide Area Networks (WANs). The image data received vianetwork interface 210 may undergo image processing processes by ISP 206.

Motion sensor interface 212 is circuitry for interfacing with motionsensor 234. Motion sensor interface 212 receives sensor information frommotion sensor 234 and processes the sensor information to determine theorientation or movement of the device 100.

Display controller 214 is circuitry for sending image data to bedisplayed on display 216. Display controller 214 receives the image datafrom ISP 206, CPU 208, graphic processor or system memory 230 andprocesses the image data into a format suitable for display on display216.

Memory controller 222 is circuitry for communicating with system memory230. Memory controller 222 may read data from system memory 230 forprocessing by ISP 206, CPU 208, GPU 220 or other subcomponents of SOCcomponent 204. Memory controller 222 may also write data to systemmemory 230 received from various subcomponents of SOC component 204.

Video encoder 224 is hardware, software, firmware or a combinationthereof for encoding video data into a format suitable for storing inpersistent storage 128 or for passing the data to network interface w10for transmission over a network to another device.

In some embodiments, one or more subcomponents of SOC component 204 orsome functionality of these subcomponents may be performed by softwarecomponents executed on ISP 206, CPU 208 or GPU 220. Such softwarecomponents may be stored in system memory 230, persistent storage 228 oranother device communicating with device 100 via network interface 210.

Image data or video data may flow through various data paths within SOCcomponent 204. In one example, raw image data may be generated from theimage sensors 202 and processed by ISP 206, and then sent to systemmemory 230 via bus 232 and memory controller 222. After the image datais stored in system memory 230, it may be accessed by video encoder 224for encoding or by display 116 for displaying via bus 232.

In another example, image data is received from sources other than theimage sensors 202. For example, video data may be streamed, downloaded,or otherwise communicated to the SOC component 204 via wired or wirelessnetwork. The image data may be received via network interface 210 andwritten to system memory 230 via memory controller 222. The image datamay then be obtained by ISP 206 from system memory 230 and processedthrough one or more image processing pipeline stages, as described belowin detail with reference to FIG. 3 . The image data may then be returnedto system memory 230 or be sent to video encoder 224, display controller214 (for display on display 216), or storage controller 226 for storageat persistent storage 228.

Example Image Signal Processing Pipelines

FIG. 3 is a block diagram illustrating image processing pipelinesimplemented using ISP 206, according to one embodiment. In theembodiment of FIG. 3 , ISP 206 is coupled to an image sensor system 201that includes one or more image sensors 202A through 202N (hereinaftercollectively referred to as “image sensors 202” or also referredindividually as “image sensor 202”) to receive raw image data. The imagesensor system 201 may include one or more sub-systems that control theimage sensors 202 individually. In some cases, each image sensor 202 mayoperate independently while, in other cases, the image sensors 202 mayshare some components. For example, in one embodiment, two or more imagesensors 202 may be share the same circuit board that controls themechanical components of the image sensors (e.g., actuators that changethe lens positions of each image sensor). The image sensing componentsof an image sensor 202 may include different types of image sensingcomponents that may provide raw image data in different forms to the ISP206. For example, in one embodiment, the image sensing components mayinclude focus pixels that are used for auto-focusing and image pixelsthat are used for capturing images. In another embodiment, the imagesensing pixels may be used for both auto-focusing and image capturingpurposes.

ISP 206 implements an image processing pipeline which may include a setof stages that process image information from creation, capture orreceipt to output. ISP 206 may include, among other components, sensorinterface 302, central control 320, front-end pipeline stages 330,back-end pipeline stages 340, image statistics module 304, vision module322, back-end interface 342, output interface 316, and auto-focuscircuits 350A through 350N (hereinafter collectively referred to as“auto-focus circuits 350” or referred individually as “auto-focuscircuits 350”). ISP 206 may include other components not illustrated inFIG. 3 or may omit one or more components illustrated in FIG. 3 .

In one or more embodiments, different components of ISP 206 processimage data at different rates. In the embodiment of FIG. 3 , front-endpipeline stages 330 (e.g., raw processing stage 306 and resampleprocessing stage 308) may process image data at an initial rate. Thus,the various different techniques, adjustments, modifications, or otherprocessing operations performed by these front-end pipeline stages 330at the initial rate. For example, if the front-end pipeline stages 330process 2 pixels per clock cycle, then raw processing stage 306operations (e.g., black level compensation, highlight recovery anddefective pixel correction) may process 2 pixels of image data at atime. In contrast, one or more back-end pipeline stages 340 may processimage data at a different rate less than the initial data rate. Forexample, in the embodiment of FIG. 3 , back-end pipeline stages 340(e.g., noise processing stage 310, color processing stage 312, andoutput rescale 314) may be processed at a reduced rate (e.g., 1 pixelper clock cycle).

Raw image data captured by image sensors 202 may be transmitted todifferent components of ISP 206 in different manners. In one embodiment,raw image data corresponding to the focus pixels may be sent to theauto-focus circuits 350 while raw image data corresponding to the imagepixels may be sent to the sensor interface 302. In another embodiment,raw image data corresponding to both types of pixels may simultaneouslybe sent to both the auto-focus circuits 350 and the sensor interface302.

Auto-focus circuits 350 may include hardware circuit that analyzes rawimage data to determine an appropriate lens position of each imagesensor 202. In one embodiment, the raw image data may include data thatis transmitted from image sensing pixels that specializes in imagefocusing. In another embodiment, raw image data from image capturepixels may also be used for auto-focusing purpose. An auto-focus circuit350 may perform various image processing operations to generate datathat determines the appropriate lens position. The image processingoperations may include cropping, binning, image compensation, scaling togenerate data that is used for auto-focusing purpose. The auto-focusingdata generated by auto-focus circuits 350 may be fed back to the imagesensor system 201 to control the lens positions of the image sensors202. For example, an image sensor 202 may include a control circuit thatanalyzes the auto-focusing data to determine a command signal that issent to an actuator associated with the lens system of the image sensorto change the lens position of the image sensor. The data generated bythe auto-focus circuits 350 may also be sent to other components of theISP 206 for other image processing purposes. For example, some of thedata may be sent to image statistics 304 to determine informationregarding auto-exposure.

The auto-focus circuits 350 may be individual circuits that are separatefrom other components such as image statistics 304, sensor interface302, front-end 330 and back-end 340. This allows the ISP 206 to performauto-focusing analysis independent of other image processing pipelines.For example, the ISP 206 may analyze raw image data from the imagesensor 202A to adjust the lens position of image sensor 202A using theauto-focus circuit 350A while performing downstream image processing ofthe image data from image sensor 202B simultaneously. In one embodiment,the number of auto-focus circuits 350 may correspond to the number ofimage sensors 202. In other words, each image sensor 202 may have acorresponding auto-focus circuit that is dedicated to the auto-focusingof the image sensor 202. The device 100 may perform auto focusing fordifferent image sensors 202 even if one or more image sensors 202 arenot in active use. This allows a seamless transition between two imagesensors 202 when the device 100 switches from one image sensor 202 toanother. For example, in one embodiment, a device 100 may include awide-angle camera and a telephoto camera as a dual back camera systemfor photo and image processing. The device 100 may display imagescaptured by one of the dual cameras and may switch between the twocameras from time to time. The displayed images may seamless transitionfrom image data captured by one image sensor 202 to image data capturedby another image sensor without waiting for the second image sensor 202to adjust its lens position because two or more auto-focus circuits 350may continuously provide auto-focus data to the image sensor system 201.

Raw image data captured by different image sensors 202 may also betransmitted to sensor interface 302. Sensor interface 302 receives rawimage data from image sensor 202 and processes the raw image data intoan image data processable by other stages in the pipeline. Sensorinterface 302 may perform various preprocessing operations, such asimage cropping, binning or scaling to reduce image data size. In someembodiments, pixels are sent from the image sensor 202 to sensorinterface 302 in raster order (e.g., horizontally, line by line). Thesubsequent processes in the pipeline may also be performed in rasterorder and the result may also be output in raster order. Although only asingle image sensor and a single sensor interface 302 are illustrated inFIG. 3 , when more than one image sensor is provided in device 100, acorresponding number of sensor interfaces may be provided in ISP 206 toprocess raw image data from each image sensor.

Front-end pipeline stages 330 process image data in raw or full-colordomains. Front-end pipeline stages 330 may include, but are not limitedto, raw processing stage 306 and resample processing stage 308. A rawimage data may be in Bayer raw format, for example. In Bayer raw imageformat, pixel data with values specific to a particular color (insteadof all colors) is provided in each pixel. In an image capturing sensor,image data is typically provided in a Bayer pattern. Raw processingstage 306 may process image data in a Bayer raw format.

The operations performed by raw processing stage 306 include, but arenot limited, sensor linearization, black level compensation, fixedpattern noise reduction, defective pixel correction, raw noisefiltering, lens shading correction, white balance gain, and highlightrecovery. Sensor linearization refers to mapping non-linear image datato linear space for other processing. Black level compensation refers toproviding digital gain, offset and clip independently for each colorcomponent (e.g., Gr, R, B, Gb) of the image data. Fixed pattern noisereduction refers to removing offset fixed pattern noise and gain fixedpattern noise by subtracting a dark frame from an input image andmultiplying different gains to pixels. Defective pixel correction refersto detecting defective pixels, and then replacing defective pixelvalues. Raw noise filtering refers to reducing noise of image data byaveraging neighbor pixels that are similar in brightness. Highlightrecovery refers to estimating pixel values for those pixels that areclipped (or nearly clipped) from other channels. Lens shading correctionrefers to applying a gain per pixel to compensate for a dropoff inintensity roughly proportional to a distance from a lens optical center.White balance gain refers to providing digital gains for white balance,offset and clip independently for all color components (e.g., Gr, R, B,Gb in Bayer format). Components of ISP 206 may convert raw image datainto image data in full-color domain, and thus, raw processing stage 306may process image data in the full-color domain in addition to orinstead of raw image data.

Resample processing stage 308 performs various operations to convert,resample, or scale image data received from raw processing stage 306.Operations performed by resample processing stage 308 may include, butnot limited to, demosaic operation, per-pixel color correctionoperation, Gamma mapping operation, color space conversion anddownscaling or sub-band splitting. Demosaic operation refers toconverting or interpolating missing color samples from raw image data(for example, in a Bayer pattern) to output image data into a full-colordomain. Demosaic operation may include low pass directional filtering onthe interpolated samples to obtain full-color pixels. Per-pixel colorcorrection operation refers to a process of performing color correctionon a per-pixel basis using information about relative noise standarddeviations of each color channel to correct color without amplifyingnoise in the image data. Gamma mapping refers to converting image datafrom input image data values to output data values to perform gammacorrection. For the purpose of Gamma mapping, lookup tables (or otherstructures that index pixel values to another value) for different colorcomponents or channels of each pixel (e.g., a separate lookup table forR, G, and B color components) may be used. Color space conversion refersto converting color space of an input image data into a differentformat. In one embodiment, resample processing stage 308 converts RGBformat into YCbCr format for further processing.

Central control module 320 may control and coordinate overall operationof other components in ISP 206. Central control module 320 performsoperations including, but not limited to, monitoring various operatingparameters (e.g., logging clock cycles, memory latency, quality ofservice, and state information), updating or managing control parametersfor other components of ISP 206, and interfacing with sensor interface302 to control the starting and stopping of other components of ISP 206.For example, central control module 320 may update programmableparameters for other components in ISP 206 while the other componentsare in an idle state. After updating the programmable parameters,central control module 320 may place these components of ISP 206 into arun state to perform one or more operations or tasks. Central controlmodule 320 may also instruct other components of ISP 206 to store imagedata (e.g., by writing to system memory 230 in FIG. 2 ) before, during,or after resample processing stage 308. In this way full-resolutionimage data in raw or full-color domain format may be stored in additionto or instead of processing the image data output from resampleprocessing stage 308 through backend pipeline stages 340.

Image statistics module 304 performs various operations to collectstatistic information associated with the image data. The operations forcollecting statistics information may include, but not limited to,sensor linearization, replace patterned defective pixels, sub-sample rawimage data, detect and replace non-patterned defective pixels, blacklevel compensation, lens shading correction, and inverse black levelcompensation. After performing one or more of such operations,statistics information such as 3A statistics (Auto white balance (AWB),auto exposure (AE), histograms (e.g., 2D color or component) and anyother image data information may be collected or tracked. In someembodiments, certain pixels' values, or areas of pixel values may beexcluded from collections of certain statistics data when precedingoperations identify clipped pixels. Although only a single statisticsmodule 304 is illustrated in FIG. 3 , multiple image statistics modulesmay be included in ISP 206. For example, each image sensor 202 maycorrespond to an individual image statistics unit 304. In suchembodiments, each statistic module may be programmed by central controlmodule 320 to collect different information for the same or differentimage data.

Vision module 322 performs various operations to facilitate computervision operations at CPU 208 such as facial detection in image data. Thevision module 322 may perform various operations includingpre-processing, global tone-mapping and Gamma correction, vision noisefiltering, resizing, keypoint detection, generation ofhistogram-of-orientation gradients (HOG) and normalized crosscorrelation (NCC). The pre-processing may include subsampling or binningoperation and computation of luminance if the input image data is not inYCrCb format. Global mapping and Gamma correction can be performed onthe pre-processed data on luminance image. Vision noise filtering isperformed to remove pixel defects and reduce noise present in the imagedata, and thereby, improve the quality and performance of subsequentcomputer vision algorithms. Such vision noise filtering may includedetecting and fixing dots or defective pixels, and performing bilateralfiltering to reduce noise by averaging neighbor pixels of similarbrightness. Various vision algorithms use images of different sizes andscales. Resizing of an image is performed, for example, by binning orlinear interpolation operation. Keypoints are locations within an imagethat are surrounded by image patches well suited to matching in otherimages of the same scene or object. Such keypoints are useful in imagealignment, computing camera pose and object tracking. Keypoint detectionrefers to the process of identifying such keypoints in an image. HOGprovides descriptions of image patches for tasks in mage analysis andcomputer vision. HOG can be generated, for example, by (i) computinghorizontal and vertical gradients using a simple difference filter, (ii)computing gradient orientations and magnitudes from the horizontal andvertical gradients, and (iii) binning the gradient orientations. NCC isthe process of computing spatial cross-correlation between a patch ofimage and a kernel.

Back-end interface 342 receives image data from other image sources thanimage sensor 102 and forwards it to other components of ISP 206 forprocessing. For example, image data may be received over a networkconnection and be stored in system memory 230. Back-end interface 342retrieves the image data stored in system memory 230 and provides it toback-end pipeline stages 340 for processing. One of many operations thatare performed by back-end interface 342 is converting the retrievedimage data to a format that can be utilized by back-end processingstages 340. For instance, back-end interface 342 may convert RGB, YCbCr4:2:0, or YCbCr 4:2:2 formatted image data into YCbCr 4:4:4 colorformat.

Back-end pipeline stages 340 processes image data according to aparticular full-color format (e.g., YCbCr 4:4:4 or RGB). In someembodiments, components of the back-end pipeline stages 340 may convertimage data to a particular full-color format before further processing.Back-end pipeline stages 340 may include, among other stages, noiseprocessing stage 310 and color processing stage 312. Back-end pipelinestages 340 may include other stages not illustrated in FIG. 3 .

Noise processing stage 310 performs various operations to reduce noisein the image data. The operations performed by noise processing stage310 include, but are not limited to, color space conversion,gamma/de-gamma mapping, temporal filtering, noise filtering, lumasharpening, and chroma noise reduction. The color space conversion mayconvert an image data from one color space format to another color spaceformat (e.g., RGB format converted to YCbCr format). Gamma/de-gammaoperation converts image data from input image data values to outputdata values to perform gamma correction or reverse gamma correction.Temporal filtering filters noise using a previously filtered image frameto reduce noise. For example, pixel values of a prior image frame arecombined with pixel values of a current image frame. Noise filtering mayinclude, for example, spatial noise filtering. Luma sharpening maysharpen luma values of pixel data while chroma suppression may attenuatechroma to gray (e.g., no color). In some embodiment, the luma sharpeningand chroma suppression may be performed simultaneously with spatial nosefiltering. The aggressiveness of noise filtering may be determineddifferently for different regions of an image. Spatial noise filteringmay be included as part of a temporal loop implementing temporalfiltering. For example, a previous image frame may be processed by atemporal filter and a spatial noise filter before being stored as areference frame for a next image frame to be processed. In otherembodiments, spatial noise filtering may not be included as part of thetemporal loop for temporal filtering (e.g., the spatial noise filter maybe applied to an image frame after it is stored as a reference imageframe and thus the reference frame is not spatially filtered.

Color processing stage 312 may perform various operations associatedwith adjusting color information in the image data. The operationsperformed in color processing stage 312 include, but are not limited to,local tone mapping, gain/offset/clip, color correction,three-dimensional color lookup, gamma conversion, and color spaceconversion. Local tone mapping refers to spatially varying local tonecurves in order to provide more control when rendering an image. Forinstance, a two-dimensional grid of tone curves (which may be programmedby the central control module 320) may be bi-linearly interpolated suchthat smoothly varying tone curves are created across an image. In someembodiments, local tone mapping may also apply spatially varying andintensity varying color correction matrices, which may, for example, beused to make skies bluer while turning down blue in the shadows in animage. Digital gain/offset/clip may be provided for each color channelor component of image data. Color correction may apply a colorcorrection transform matrix to image data. 3D color lookup may utilize athree dimensional array of color component output values (e.g., R, G, B)to perform advanced tone mapping, color space conversions, and othercolor transforms. Gamma conversion may be performed, for example, bymapping input image data values to output data values in order toperform gamma correction, tone mapping, or histogram matching. Colorspace conversion may be implemented to convert image data from one colorspace to another (e.g., RGB to YCbCr). Other processing techniques mayalso be performed as part of color processing stage 312 to perform otherspecial image effects, including black and white conversion, sepia toneconversion, negative conversion, or solarize conversion.

Output rescale module 314 may resample, transform and correct distortionon the fly as the ISP 206 processes image data. Output rescale module314 may compute a fractional input coordinate for each pixel and usesthis fractional coordinate to interpolate an output pixel via apolyphase resampling filter. A fractional input coordinate may beproduced from a variety of possible transforms of an output coordinate,such as resizing or cropping an image (e.g., via a simple horizontal andvertical scaling transform), rotating and shearing an image (e.g., vianon-separable matrix transforms), perspective warping (e.g., via anadditional depth transform) and per-pixel perspective divides applied inpiecewise in strips to account for changes in image sensor during imagedata capture (e.g., due to a rolling shutter), and geometric distortioncorrection (e.g., via computing a radial distance from the opticalcenter in order to index an interpolated radial gain table, and applyinga radial perturbance to a coordinate to account for a radial lensdistortion).

Output rescale module 314 may apply transforms to image data as it isprocessed at output rescale module 314. Output rescale module 314 mayinclude horizontal and vertical scaling components. The vertical portionof the design may implement series of image data line buffers to holdthe “support” needed by the vertical filter. As ISP 206 may be astreaming device, it may be that only the lines of image data in afinite-length sliding window of lines are available for the filter touse. Once a line has been discarded to make room for a new incomingline, the line may be unavailable. Output rescale module 314 maystatistically monitor computed input Y coordinates over previous linesand use it to compute an optimal set of lines to hold in the verticalsupport window. For each subsequent line, output rescale module mayautomatically generate a guess as to the center of the vertical supportwindow. In some embodiments, output rescale module 314 may implement atable of piecewise perspective transforms encoded as digital differenceanalyzer (DDA) steppers to perform a per-pixel perspectivetransformation between a input image data and output image data in orderto correct artifacts and motion caused by sensor motion during thecapture of the image frame. Output rescale may provide image data viaoutput interface 316 to various other components of device 100, asdiscussed above with regard to FIGS. 1 and 2 .

In various embodiments, the functionally of components 302 through 350may be performed in a different order than the order implied by theorder of these functional units in the image processing pipelineillustrated in FIG. 3 , or may be performed by different functionalcomponents than those illustrated in FIG. 3 . Moreover, the variouscomponents as described in FIG. 3 may be embodied in variouscombinations of hardware, firmware or software.

Example Pipelines for Image Fusion

FIG. 4 is a block diagram illustrating a portion of the image processingpipeline including circuitry for feature extraction, according to oneembodiment. Images 402, 404 are captured by image sensor system 201 andpassed onto vision module 322. In one embodiment, image 402 is capturedshortly before or after capturing image 404. Alternatively, images 402and 404 are captured at the same time using two different image sensors202 with different exposure times. Image 402 captures a scene with afirst exposure time, and image 404 captures the same scene with a secondexposure time that may be different than the first exposure time. If thesecond exposure time is shorter than the first exposure time, image 402can be referred to as “long exposure image” and image 404 can bereferred to as “short exposure image.” Each image 402, 404 includesmultiple color components, e.g., luma and chroma color components. Image402 is passed onto feature extractor circuit 406 of vision module 322for processing and feature extraction. Image 404 may be passed ontofeature extractor circuit 410 of vision module 322 for processing andfeature extraction. Alternatively, feature extractor circuit 410 may beturned off.

Feature extractor circuit 406 extracts first keypoint information 408about first keypoints (e.g., salient points) in image 402 by processingpixel values of pixels in image 402. The first keypoints are related tocertain distinguishable features (also referred to “salient points”) inimage 402. Extracted first keypoint information 408 can includeinformation about spatial locations (e.g., coordinates) of at least asubset of pixels in image 402 associated with the first keypoints ofimage 402. For each of the first keypoints in image 402, featureextractor circuit 406 may also extract and encode a keypoint descriptor,which includes a keypoint scale and orientation information. Thus, firstkeypoint information 408 extracted by feature extractor circuit 406 mayinclude information about a spatial location of each of the firstkeypoints of image 402 and a keypoint descriptor of each of the firstkeypoints of image 402. First keypoint information 408 associated withat least the subset of pixels of image 402 is passed onto CPU 208 forprocessing.

Feature extractor circuit 410 extracts second keypoint information 412about second keypoints in image 404 by processing pixel values of pixelsin image 404. The second keypoints are related to certaindistinguishable features (e.g., salient points) in image 404. Extractedsecond keypoint information 412 can include information about spatiallocations (e.g., coordinates) of at least a subset of pixels in image404 associated with the second keypoints of image 404. For each of thesecond keypoints in image 404, feature extractor circuit 410 may alsoextract and encode a keypoint descriptor, which includes a keypointscale and orientation information. Thus, second keypoint information 412extracted by feature extractor circuit 410 may include information abouta spatial location of each of the second keypoints of image 404 and akeypoint descriptor of each of the second keypoints of image 404. Secondkeypoint information 412 associated with at least the subset of pixelsof image 404 are passed onto CPU 208 for processing. Alternatively (notshown in FIG. 4 ), feature extractor circuit 410 is turned off. In suchcase, second keypoints of image 404 are not extracted and only firstkeypoint information 408 is passed onto CPU 208 for processing.

CPU 208 builds a model describing correspondence between image 402 andimage 404. CPU 208 searches for correspondences between first keypointinformation 408 of image 402 and second keypoint information 412 ofimage 404 to generate at least one motion vector representing relativemovement in image 402 and image 404. In one embodiment, CPU 208correlates (matches) first keypoint information 408 with second keypointinformation 412, e.g., by comparing and pairing keypoint descriptorsextracted from images 402 and 404 to determine a set of keypointinformation matches, such as pairs of keypoint descriptors extractedfrom images 402 and 404. CPU 208 then performs a model fitting algorithmby processing the determined set of keypoint information matches tobuild the model. The model fitting algorithm may be designed to discardfalse matches during the model building process. The model fittingalgorithm may be based on, e.g., the iterative random sample consensus(RANSAC) algorithm. The model built by CPU 208 includes informationabout mapping between pixels in the images 402 and 404. The model mayrepresent a linear, affine and perspective transformation.Alternatively, the model may be a non-linear transformation. Based onthe model, warping parameters (mapping information) 418 may be generatedby CPU 208 and sent to warping circuit 428 for spatial transformation ofimage 402 and/or image 404. Warping parameters 418 can be used in a formof a matrix for spatial transformation (e.g., warping) of image 402and/or image 404. The matrix for spatial transformation represents ageometric transformation matrix or a mesh grid with motion vectorsdefined for every grid point. Alternatively, a dedicated circuit insteadof CPU 208 may be provided to perform the RANSAC algorithm and togenerate warping parameters 418.

In the embodiment when feature extractor circuit 410 is turned off andonly first keypoint information 408 is passed onto CPU 208, CPU 208generates a motion vector for each of the first keypoints of image 402.This is done by performing, e.g., the NCC search within an expected andconfigurable displacement range to determine a best feature match withina defined spatial vicinity (patch) of each first keypoint of image 402.In such case, CPU 208 performs a model fitting algorithm (e.g., theRANSAC algorithm) that uses first keypoint information 408 (e.g.,coordinates of the first keypoints) and corresponding motion vectorsdetermined based on feature matches to build a model, whereas matchingof keypoints between images 402 and 404 is not performed. The modelfitting algorithm may be designed to discard false feature matches.Based on the built model, CPU 208 generates warping parameters (mappinginformation) 418 that is sent to warping circuit 428 for spatialtransformation of image 402. Alternatively, a dedicated circuit insteadof CPU 208 may be provided to perform the NCC search and to generate amotion vector for each of the first keypoints of image 402. In suchcase, CPU 208 uses the motion vector for each of the first keypointsgenerated by the dedicated circuit to build the model.

Image 402, which may be a long exposure image, is also passed onto imageenhancement processor 420 that performs certain processing of image 402,e.g., noise removal, enhancement, etc., to obtain processed version 422of image 402. Processed version 422 is passed onto clipping markercircuit 424. Clipping marker circuit 424 identifies clipped (e.g.,oversaturated) pixels in processed version 422 of image 402 having oneor more color component values that exceed threshold values as clippingmarkers. Clipping marker circuit 424 may replace the pixel values withpredetermined pixel values so that any of these pixels or any otherpixel derived from these pixels downstream from clipping marker circuit424 can be identified and addressed appropriately in subsequentprocessing, such as corresponding morphological operations (e.g.,erosion or dilation) of the clipping markers. For example, themorphological operations can be conducted during a warping operationperformed by warping circuit 428, during a pyramid generation performedby pyramid generator circuit 432, and/or during a fusion operationperformed by image fusion processing module 444.

Warping circuit 428 accommodates the linear and non-lineartransformations defined by the model generated by CPU 208. Warpingcircuit 428 warps processed image 426 using the mapping informationaccording to the warping parameters 418 to generate warped version 430of image 402 (warped image 430) spatially more aligned to image 404 thanto image 402. Alternatively (not shown in FIG. 4 ), warping circuit 428warps image 404 using the mapping information in model 418 to generatewarped version 430 of image 404 spatially more aligned to image 402 thanto image 404. Warped image 430 generated by warping circuit 428 is thenpassed onto pyramid generator circuit 432.

Pyramid generator circuit 432 generates multiple downscaled warpedimages each having a different resolution by sequentially downscalingwarped image 430. Each downscaled warped image includes the multiplecolor components. The downscaled warped images obtained from warpedimage 430 may be stored in e.g., system memory 230 (not shown in FIG. 4). Low frequency components of the downscaled warped images and a lowfrequency component of an unscaled single color version (e.g., lumacomponent) of warped image 430 are passed as warped image data 434 ontoimage fusion processing circuit 444 for fusion with corresponding imagedata 442 obtained from image 404. Note that in some embodiments, imageenhancement processor 420, clipping locator circuit 424, warping circuit428, and pyramid generator circuit 432 are part of noise processingstage 310. In some embodiments, one or more of image enhancementprocessor 420, clipping locator circuit 424, warping circuit 428, andpyramid generator circuit 432 are outside of noise processing stage 310,such as in another stage of back-end pipeline stages 340.

Image enhancement processor 436 performs certain processing of image 404(e.g., noise removal, enhancement, etc.) to obtain processed image 438for passing onto pyramid generator circuit 440. Image enhancementprocessor 436 may perform substantially same operations as imageenhancement processor 420. Pyramid generator circuit 440 generatesmultiple downscaled images each having a different resolution bysequentially downscaling processed image 438. Each downscaled imagegenerated by pyramid generator circuit 440 includes the multiple colorcomponents (e.g., luma and chroma components). The downscaled imagesobtained from processed image 438 may be stored in, e.g., system memory230. Low frequency components of the downscaled images and a lowfrequency component of an unscaled single color version (e.g., lumacomponent) of processed image 438 are passed onto image fusionprocessing circuit 444 as image data 442. Note that in some embodiments,image enhancement processor 436 and pyramid generator circuit 440 arepart of noise processing stage 310. In some embodiments, at least one ofimage enhancement processor 436 and pyramid generator circuit 440 isoutside of noise processing stage 310, such as in another stage ofback-end pipeline stages 340.

Image fusion processing circuit 444 performs per pixel blending betweena portion of warped image data 434 related to the unscaled single colorversion of warped image 430 with a portion of image data 442 related tothe unscaled single color version of processed image 438 to generateunscaled single color version of fused image 446. Image fusionprocessing circuit 444 also performs per pixel blending between aportion of warped image data 434 related to a downscaled warped image(obtained by downscaling warped image 430) and a portion of image data442 related to a corresponding downscaled image (obtained by downscalingprocessed image 438) to generate first downscaled version 448 of thefused image comprising the multiple color components. First downscaledversion 448 has a pixel resolution equal to a quarter of a pixelresolution of unscaled single color version 446. Unscaled single colorversion 446 and first downscaled version 448 are passed ontopost-processing circuit 450 for further processing and enhancement.

Post-processing circuit 450 performs post-processing of unscaled singlecolor version 446 and first downscaled version 448 to obtainpost-processed fused image 472. Post-processing circuit 450 may be partof color processing stage 312. Post-processing circuit 450 includessub-band splitter (SBS) circuit 452, local tone mapping (LTM) circuit458, local contrast enhancement (LCE) circuit 462, sub-band merger (SBM)circuit 466 and sharpening circuit 470. SBS circuit 452 performssub-band splitting of unscaled single color version 446 to generate highfrequency component of unscaled single color version 454 passed onto SBMcircuit 466. SBS circuit 452 also performs sub-band splitting of firstdownscaled version 448 to generate low frequency component of firstdownscaled version 456 passed onto LTM circuit 458. LTM circuit 458performs LTM operation on low frequency component of first downscaledversion 456 to generate processed version of low frequency component offirst downscaled version 460 passed onto LCE circuit 462. LCE circuit462 performs local photometric contrast enhancement of a single colorcomponent (e.g., luma component) of processed version of low frequencycomponent of first downscaled version 460 to generate enhanced versionof first downscaled version of fused image 464. SBM circuit 466 mergeshigh frequency component of unscaled single color version 454 andenhanced version of first downscaled version of fused image 464 togenerate merged fused image data 468 passed onto sharpening circuit 470.Sharpening circuit 470 performs sharpening (e.g., photometric contrastenhancement) on a single color component (e.g., luma component) ofmerged fused image data 468 to generate post-processed fused image 472.Post-processed fused image 472 can be passed to output rescale 314 andthen output interface 316. The processing performed at post-processingcircuit 450 is merely an example, and various other post-processing maybe performed as an alternative or as an addition to the processing atpost processing circuit 450.

Example Architecture for Keypoint Descriptor Processing

FIG. 5 is a block diagram of a feature extractor circuit 500, accordingto one embodiment. The feature extractor circuit 500 is an example of afeature extractor circuit 406 or 410 of the vision module 322, or thefeature extractor circuit 500 may be separate from the vision module322. The feature extractor circuit 500 performs a multi-stage process togenerate keypoint descriptors of an image 512. In stage 1, the featureextractor circuit 500 generates an image pyramid including multipleoctaves and multiple scales per octave from the received image 512.Pyramid images in different octaves have different resolutions, andpyramid images in the same octave but different scale have the sameresolution with different amounts of blurring. In stage 2, the featureextractor circuit 500 generates a response map (RM) image for each ofthe pyramid images and determines keypoint candidates using an intraoctave processing that compares RM images of the same octave. In stage3, the feature extractor circuit 500 determines keypoints from thekeypoint candidates using an inter octave processing that compares RMimages in different octaves. The feature extractor circuit 500 alsoperforms a sub-pixel refinement where the x, y, or scale values of eachkeypoint may be updated using pixel value interpolation. In stage 4, thefeature extractor circuit 500 generates keypoint descriptors of theidentified keypoints. The keypoint descriptor of a keypoint includesdescriptor comparison data defining comparison results between theintensity values of sample points of the keypoint.

The feature extractor circuit 500 may include, among other components, apyramid image generator 516, an RM generator 520, an intra octavekeypoint candidate selector 522, an inter octave keypoint selector 526,and a keypoint descriptor generator 530. These circuits are theprocessing circuitry for the stages 1 through 4. The feature extractorcircuit 500 also includes one or more memories (e.g., staticrandom-access memory (SRAM)) including a pyramid image buffer 518, an RMbuffer 524, a keypoint candidate list 532, and a keypoint candidate list528. To facilitate fast and efficient processing, the one or morememories may be located on the same IC chip as the processing circuitryof the feature extractor circuit 500.

The feature extractor circuit 500 may use a sliding window mechanismwhere processing for subsequent stages begin when enough data from theprior stage has been generated. Keypoint descriptor generation isperformed on different portions of the pyramid images at differenttimes, and the pyramid image buffer 518 stores different portions of thepyramid images at different times to facilitate the keypoint descriptorgeneration. Portions (e.g., lines) of the pyramid images that have beenprocessed for keypoint descriptor generation and are no longer neededare removed from the memory locations of the pyramid image buffer 518,and other portions of the pyramid images that need to be processed forthe keypoint descriptor generation are loaded onto the memory locationsof the pyramid image buffer 518. At any given time, the pyramid imagebuffer 518 does not need to store full pyramid images of the imagepyramid. The pyramid image buffer 518 may have a storage size that issmaller than a data size of the image pyramid.

Similarly, keypoint determination is performed on different portions ofthe RM images at different times, and the RM buffer 524 stores differentportions of the RM images at different times to facilitate the keypointdetermination. Portions of the RM images that have been processed forkeypoint selection and are no longer needed are removed from memorylocations of the RM buffer 524, and other portions of the RM images thatneed to be processed for the keypoint determination are loaded onto thememory locations of the RM buffer 524. At any given time, the RM buffer524 does not need to store full RM images. The RM buffer 524 may have astorage size that is smaller than a data size of all RM images.

The input image buffer 514 is a circuit that receives and stores theimage 512 for processing by the other components of the featureextractor circuit 500. In some embodiments, the input image buffer 514is separate from the feature extractor circuit 500.

The pyramid image generator 516 is a circuit that receives image data ofthe image 512 from the input image buffer 514 and generates the imagepyramid by repeatedly applying smoothing and downscaling functions. Theimage pyramid includes multiple octaves, with each octave includingmultiple scales. The image pyramid includes an image (referred to as a“pyramid image”) for each scale. Downscaling reduces the image size fordifferent octaves, and different amounts of smoothing is used to producedifferent scales within each octave. In one example, the image pyramidincludes five octaves and two scales for each octave. In someembodiments, the image pyramid is Laplacian pyramid, steerable pyramid,or some other type of image pyramid. Additional details regarding stage1 processing by the pyramid image generator 516 are discussed inconnection with FIG. 6 .

The pyramid image buffer 518 is a memory circuit that stores the imagedata of the image pyramid generated by the pyramid image generator 516.Rather than storing the entire image pyramid at one time, the pyramidimage buffer 518 stores only portions of the image data for the keypointdescriptor generation of the stage 4 processing. The portions of theimage pyramid stored in the image buffer 518 at any one time may includeonly some of the pixel lines of each pyramid image. The stage 4processing for keypoint descriptor generation can begin as soon asenough portions of the image data are stored in the pyramid image buffer518. Portions of the image pyramid that are no longer needed for thekeypoint descriptor generation are removed from the pyramid image buffer51 to provide space for other portions that have not been processed forkeypoint descriptor generation.

The RM generator 520 is a circuit that receives the image data of thepyramid images and generates RM image data using the image data of thepyramid images received from the pyramid image generator 516. For eachpyramid image, the RM generator 520 generates a corresponding RM imageof the same pixel size. Each pixel of a RM image includes a responsevalue. To determine the response values of a RM image, the RM generator520 applies Laplacian filters (e.g., a 1×3 Laplacian filter followed bya 3×1 Laplacian filter) to the image data of the corresponding pyramidimage. The RM generator 520 then determines response values RM of the RMimage using the Laplacian filtered results. Rather than waiting forcomplete pyramid images, the RM generator 520 can begin to generate RMimages when enough lines (e.g., 3 lines for the Laplacian filter) of thecorresponding pyramid images are loaded onto the pyramid image buffer518.

The intra octave keypoint candidate selector 522 is a circuit thatdetermines keypoint candidates and partially validates the keypointcandidates by performing intra octave non-maxima suppression (NMS).While the RM generator 520 generates line n of an RM image, the intraoctave keypoint candidate selector 522 finds keypoint candidates in linen−1 (e.g., the previously generated line of the RM image). A keypointcandidate is found by passing the following criteria: (1) the absolutevalue of an RM pixel is greater than a threshold value, (2) the RM pixelvalue is a local minimum or a local maximum (NMS) in a surrounding 2×3×3pixel box, and optionally (3) the RM pixel passes a determinantcriterion. For each keypoint candidate, the intra octave keypointcandidate selector 522 generates keypoint parameters including x and yimage locations (defined in the original image resolution), a scalevalue s defining the scale of the keypoint candidate, and an RM valuedefining the RM pixel value of the keypoint candidate. Additionaldetails regarding stage 2 processing by the RM generator 520 and intraoctave keypoint candidate selector 522 are discussed in connection withFIG. 7 .

The RM buffer 524 is a memory that stores the RM image data of the RMimages generated by the RM generator 520. Rather than storing entire RMimages at one time, the RM buffer 524 stores portions of the RM imagesdata needed for keypoint determination of the stage 3 processing. Theportions of the RM images stored in the RM buffer 524 at any one timemay include only some pixel lines of each RM image. The stage 3processing for keypoint determination can begin as soon as enoughportions of the image data are loaded onto the RM buffer 524. Portionsof the RM images that are no longer needed for the keypointdetermination are removed from the RM buffer 524 to provide space forother portions that are still to be processed for the keypointdetermination.

The keypoint candidate list 532 is a memory circuit that stores thekeypoint candidates determined by the intra octave keypoint candidateselector 522. Each scale may include keypoint candidates. Each keypointcandidate is defined by the keypoint parameters, which are stored in thekeypoint candidate list 532.

The inter octave keypoint selector 526 determines keypoints from thekeypoint candidates stored in the keypoint candidate list 532 andperforms sub-pixel refinement for the determined keypoints. The interoctave keypoint selector 526 uses RM pixel values of RM images stored inthe RM buffer 524 to perform the keypoint determination and sub-pixelrefinement. For each keypoint candidate, the inter octave keypointselector 526 performs NMS using the 3×3 pixel plane in the adjacentoctave. The keypoint candidate is validated as a keypoint if the RMpixel value of the keypoint candidate is larger or smaller than theneighboring 9 RM pixels of the 3×3 pixel plane in the adjacent octave.The sub-pixel refinement is performed to update the keypoint parametersof the determined keypoint. The inter octave keypoint selector 526 uses2×3×3 pixel values from the current octave RM images. For each of thetwo scales in the octave, the immediate lower and higher scale is usedto perform the sub-pixel refinement operation to generate the updatedkeypoint parameters. The updated keypoint parameters include possibleupdates to the x and y locations, a keypoint scale value (e.g., whichmay be different from the keypoint scale s), and a Laplacian score.Additional details regarding stage 3 processing by the inter octavekeypoint selector 526 are discussed in connection with FIG. 8 .

The keypoint list 528 is a memory circuit that stores the keypointsdetermined by the inter octave keypoint selector 526. Each keypoint isdefined by the keypoint parameters, which may be updated by the interoctave keypoint selector 526 using the sub-pixel refinement.

The keypoint descriptor generator 530 is a circuit that generates akeypoint descriptor (e.g., a Fast Retina Keypoint (FREAK) descriptor)for each keypoint. The keypoint descriptor generator 530 determinesdescriptor comparison data for each keypoint and combines the descriptorcomparison data with other keypoint parameters to generate the fullkeypoint descriptor. The keypoint descriptor generator 530 receivesimage data of the pyramid images from the pyramid image buffer 518 andkeypoints from the keypoint list 528 and determines sample points foreach keypoint. For each keypoint, the keypoint descriptor generator 530determines an orientation angle θ by calculating difference in intensityvalues of the sample points, divided by the vector direction, of (e.g.,30) pixels around the keypoint. For each keypoint, the keypointdescriptor generator 530 rotates a set of (e.g., 43) pixels around thekeypoint by the orientation angle θ of the keypoint to generate thedescriptor comparison data defining the comparison results of a set of(e.g., 471) comparisons between pairs of sample points. The keypointdescriptors are sent (e.g., via a direct memory access (DMA)) to thesystem memory 230 for sharing with the CPU 208. Additional detailsregarding stage 4 processing by the keypoint descriptor generator 530are discussed in connection with FIG. 9 .

FIG. 6 is a block diagram of a pyramid image generator 516, according toone embodiment. The pyramid image generator 516 generates pyramid imagesP0 through P9 using image data of the image 512 stored in the inputimage buffer 514. The image pyramid may be a gaussian pyramid where thesmoothing is performed using a gaussian function as defined by:

$\begin{matrix}{{G( {x,y} )} = {\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{-}\frac{x^{2} + y^{2}}{2\sigma^{2}}}} & {{Equation}1}\end{matrix}$

where x is the pixel distance from the origin in the horizontal axis, yis the pixel distance from the origin in the vertical axis, and σ is thestandard deviation of the Gaussian distribution.

Pyramid images P0 and P1 are two scales in the lowest octave, P2 and P3are two scales in the next octave, P4 and P5 are two scales in the nextoctave, P6 and P7 are two scales in the next octave, and P8 and P9 aretwo scales in the highest octave. For each octave, the pyramid images inthe next higher octave have half the resolution in width and height. Togenerate the pyramid images, the pyramid image generator 516 includesblur filters 610A through 610J (individually referred to as blur filter610), spatial filters 612A through 612D (individually referred to asspatial filter 612), decimators 614A through 614D (individuallydecimator 614), and image buffers 616A through 616D (individuallyreferred to as image buffer 616). The blur filters 610 are each a filterthat applies the gaussian function to smooth the image data. Differentfilters may use different a values to apply different amounts ofsmoothing.

For image data at the first (lowest) octave, the blur filter 610Aapplies a gaussian filter with σ=1 to generate the pyramid image P0using the image data of the image 512 from the input image buffer 514.The blur filter 610B applies a gaussian filter with σ=√{square root over(2)} to generate the pyramid image P1 using the image data of the image512 stored in the input image buffer 514. To generate image data for thesecond octave, the spatial filter 612A and decimator 614A are applied tothe image data of the image 512 from the input image buffer 514 togenerate a buffer image 622 that is stored in the image buffer 616A. Thebuffer image 622 has half the resolution of the image 512.

For the image data at the second octave, the blur filter 610C applies agaussian filter with σ=√{square root over (7/8)} to generate the pyramidimage P2 from the image data in the image buffer 616A. The blur filter610D applies a gaussian filter with σ=√{square root over (15/8)} togenerate the pyramid image P3 from the image data stored in the imagebuffer 616A. To generate image data for the third octave, the spatialfilter 612B and decimator 614B are applied to the image data of thebuffer image 622 to generate a buffer image 624. The buffer image 624has half the resolution of the buffer image 622. The buffer image 624 isstored in the image buffer 616B.

The blur filters 610 for each octave can begin to operate as soon asenough lines of the buffer image are in the image buffer 616 of theoctave. For example, a 9×9 pixel size blur filter 910C may operate whenthere are at least 9 lines of the buffer image 622 in the image buffer616A. Similarly, the spatial filter 612 and decimator 614 for eachoctave can begin as soon as enough lines of the buffer image are in theimage buffer 616 of the octave. For example, if the spatial filter 612Buses a 2×2 pixel size filter, then generating image data for the nextoctave can begin when there are 2 lines of the buffer image 622 in theimage buffer 616A. If the spatial filter 612B uses a 3×3 pixel sizefilter, then generating image data for the next octave can begin whenthere are 3 lines in the image buffer 616A. In view of the spatialfilter 612B having a smaller filter size than the blur filters 610C and610D, the spatial filter 612B can begin operation before the blurfilters 610C and 610D. Furthermore, the time delay for generating imagedata of the next octave is reduced because the spatial filter 612B anddecimator 614B operate in parallel with the blur filters 610C and 610D.The operation at each of the subsequent octaves work similarly.

For the image data at the third octave, the blur filter 610E applies agaussian filter with σ=√{square root over (7/8)} to generate the pyramidimage P4 from the image data in the image buffer 616B. The blur filter610F applies a gaussian filter with σ=√{square root over (15/8)} togenerate the pyramid image P5 from the image data stored in the imagebuffer 616B. To generate image data for the fourth octave, the spatialfilter 612C and decimator 614C are applied to the image data of thebuffer image 624 stored in the image buffer 616B to generate a bufferimage 626 that is stored in the image buffer 616C. The buffer image 626has half the resolution of the buffer image 624.

For the image data at the fourth octave, the blur filter 610G applies agaussian filter with σ=√{square root over (7/8)} to generate the pyramidimage P6 from the image data in the image buffer 616C. The blur filter610H applies a gaussian filter with σ=√{square root over (15/8)} togenerate the pyramid image P7 from the image data stored in the imagebuffer 616C. To generate image data for the fifth octave, the spatialfilter 612D and decimator 614D are applied to the image data of thebuffer image 626 stored in the image buffer 616C to generate a bufferimage 628 that is stored in the image buffer 616D. The buffer image 628has half the resolution of the buffer image 626.

For the image data at the fifth octave, the blur filter 610I applies agaussian filter with σ=√{square root over (7/8)} to generate the pyramidimage P8 from the image data in the image buffer 616D. The blur filter610J applies a gaussian filter with σ=√{square root over (15/8)} togenerate the pyramid image P9 from the image data stored in the imagebuffer 616D.

FIG. 7 is a block diagram of RM generator 520 and intra octave keypointcandidate selector 522 for a single octave, according to one embodiment.The RM generator 520 includes an RM pre-buffer 712A, an RM filter 714A,and a response value calculator 716A that processes image data of apyramid image Pn of the nth scale to generate an RM image for the nthscale. The RM generator 520 also includes an RM pre-buffer 712B, an RMfilter 714B, and a response value calculator 716B that processes imagedata of a pyramid image Pn+1 of the adjacent n+1th scale in the sameoctave to generate an RM image for the n+1th scale.

The RM pre-buffer 712A receives the image data of the pyramid image Pnfrom the pyramid image generator 516 and stores the image data for RMimage generation. The RM filter 714A applies Laplacian filters (e.g., a1×3 Laplacian filter (Lxx) followed by a 3×1 Laplacian filter (Lyy)) tothe image data stored in the RM pre-buffer 712A. The response valuecalculator 714A determines response values 732 of the RM image using theLaplacian filtered results using a normalization factor to normalize theRM results across the different scales and octaves. The RM pre-buffer712A may store lines of the pyramid image Pn as they are generated bythe pyramid image generator 516, and the RM filter 714A may beginoperation when enough lines (e.g., 3 lines for the 3×3 Laplacian filter)are stored in the RM pre-buffer 712A. The RM pre-buffer 712B, RM filter714B, and response value calculator 716B operate the same way for theadjacent scale of the same octave to determine response values 734. Theresponse values 732 and 734 are provided to the RM buffer 524 and theintra octave keypoint candidate selector 522.

The intra octave keypoint candidate selector 522 may include, amongother components, an RM buffer 716, a threshold comparator 718, an intraoctave NMS 718, and a determinant tester 720. While the RM generator 520generates a pixel line of an RM image, the intra octave keypointcandidate selector 522 finds keypoint candidates in the previouslygenerated pixel line of the RM image.

The RM buffer 716 is a memory circuit that stores the response values732 and 734 of the RM image for keypoint candidate determination. Akeypoint candidate is found by passing the following criteria: (1) theabsolute value of an RM pixel is greater than a threshold value, (2) theRM pixel value is a local minimum or a local maximum (NMS) in asurrounding 2×3×3 pixel box, and optionally (3) the RM pixel passes adeterminant criterion.

The threshold comparator 718 is a circuit that compares the absolutevalues of the RM pixels to the threshold value. RM pixels that satisfythe threshold value are determined as potential keypoint candidates andare provided to the intra octave NMS 718. Different portions of an RMimage may use different threshold values.

The intra octave NMS 720 is a circuit that receives response values 732and 734 for the RM image of the octave from the RM buffer 716. Theresponse values 732 and 734 are used to obtain the 2×3×3 pixel boxsurround each RM pixel that satisfied the threshold value. The intraoctave NMS 720 compares each RM pixel that satisfies the threshold valueto its neighboring 8 RM pixels of the same scale, and to the neighboring9 (3×3) RM pixels in the adjacent scale of the same octave. If the RMpixel is either larger than all its 17 neighbors or smaller than all its17 neighbor RM pixels, the RM pixel passes the NMS criterion and remainsa keypoint candidate.

The determinant tester 722 applies a determinant criterion that verifiesthat a keypoint candidate is a corner by using the neighboring 8 RMpixels of the same scale. The determinant tester 722 throws out keypointcandidates that are less likely to be a corner and more likely to be ablob, which is a worse feature. The determinant test is applied usingthe following method, computed on the RM image: 3×1, 1×3 and 3×3 secondderivative filters are applied to provide RMxx, RMyy and RMxy. Thedeterminant of the RM is determined by calculating a determinant valueand a trace square RM value based on based on RMxx, RMyy and RMxy.

The determinant test passes when the trace square RM value divided bythe absolute value of the determinant value is less than a thresholdvalue. The determinant tester 722 provides keypoint candidates thatsatisfy the determinant criterion to the keypoint candidate list 532.

The components of the RM generator 520 and intra octave keypointcandidate selector 522 shown in FIG. 7 process a single octave. The RMgenerator 520 and intra octave keypoint candidate selector 522 may eachinclude additional instances of the shown components to process each ofthe octaves of the image pyramid. As such, RM images and keypointcandidates are generated for each of the scales of image pyramid.

FIG. 8 is a block diagram of the inter octave keypoint selector 526,according to one embodiment. The inter octave keypoint selector 526includes a keypoint validator 812 and a sub-pixel refiner 814. Thekeypoint validator 812 receives keypoint candidates from the keypointcandidate list 532 and RM images from the RM buffer 524. For eachkeypoint candidate of a scale, the keypoint validator 812 performs interoctave NMS using a 3×3 pixel plane in the adjacent octave. The keypointcandidate is in a scale called a keypoint scale s (also referred to as“physical scale”). The keypoint scale s is between the 3×3 pixel planeof the same octave used for intra octave NMS and the adjacent 3×3 pixelplane of the adjacent octave used for the inter octave NMS. The 3×3pixel plane in the adjacent octave is in the lower octave if thekeypoint scale s is even (e.g., P0, P2, P4, P6, or P8) or the higheroctave if the keypoint scale s is odd (e.g., P1, P3, P5, P7, or P9). Ifthe 3×3 pixel plane is in the higher octave, the 3×3 pixel plane isinterpolated to get an equivalent 3×3 pixel plane. If the 3×3 pixelplane is in the lower octave, the 3×3 pixel plane is down sampled to getan equivalent 3×3 pixel plane. The keypoint candidate is validated as akeypoint if the RM pixel value of the keypoint candidate is larger orsmaller than the neighboring 9 RM pixels of the 3×3 pixel plane in theadjacent octave. Otherwise, the keypoint candidate is discarded. Thekeypoint validator 812 provides the validated keypoints to the sub-pixelrefiner 814.

The sub-pixel refiner 814 updates the keypoint parameters of thevalidated keypoints. The updated keypoint parameters include updated xand y locations, a keypoint scale value (also referred to as “KP.Scale”)that may be different from the keypoint scale s, and a Laplacian scorevalue. For each keypoint, the sub-pixel refiner 814 reads 2×3×3 RM pixelvalues from the scales of the octave containing the keypoint. For eachof the two scales in the octave, the sub-pixel refiner 814 uses thescale and the immediate lower and higher scales (three scales) toperform the sub-pixel refinement operation. Thus, one scale from outsideof the octave is always being used. If the keypoint scale s is even,then the s−1 scale is down sampled. If the keypoint scale s is odd, thenthe s+1 scale is up sampled. The sub-pixel refiner 814 calculates firstorder derivatives Ds, Dx, and Dy and second order derivatives Dss, Dxx,Dyy, and Dxy from the 3×3×3 RM values of the three scales, and usesthese values to calculate sub-pixel values for 3 dimensions (scale, x,y).

The keypoint scale value may be calculated separately and in parallelwith the x and y values to reduce the complexity of the calculations.The keypoint scale value may be calculated based on Ds and Dss.

The keypoint scale value includes a sub-scale value that may be positiveor negative, which defines an adjustment to the keypoint scale s. If thesub-scale value is positive, then the keypoint scale value is greaterthan the keypoint scale s. If the sub-scale value is positive, then thekeypoint scale value is less than the keypoint scale s.

The sub-pixel refiner 814 determines the Laplacian score using bilinearinterpolation of the updated x and y values. The Laplacian score is theRM pixel value of the keypoint at the refined x and y values and thekeypoint scale value. After determining the updated keypoint parametersincluding the x and y values, the keypoint scale value, and theLaplacian score for a keypoint, the sub-pixel refiner 814 provides thekeypoint and the updated keypoint parameters to the keypoint list 528.Lines of the RM image data that are no longer needed for keypointselection may be removed from the RM buffer 524 to make room foradditional lines, which are then similarly processed for keypointselection by the inter octave keypoint selector 526.

FIG. 9 is a block diagram of the keypoint descriptor generator 530,according to one embodiment. The keypoint generator 530 determines anorientation angle θ of each keypoint and generates descriptor comparisondata of the keypoint using the orientation angle θ of the keypoint. Thedescriptor comparison data of each keypoint is combined with thekeypoint parameters of the keypoint to generate the complete keypointdescriptor (e.g., FREAK descriptor). The keypoint descriptor generator530 includes a point sampler 912, an intensity value calculator 914, anorientation calculator 916, a point rotator 918, a point comparator 920,an intensity value calculator 924, and a descriptor generator controller926.

The point sampler 912 receives the keypoints from the keypoint list 528and the image data of the pyramid images from the pyramid image buffer518 and samples a patch of image data for each sample point of eachkeypoint. For orientation angle θ calculation of each keypoint, thepoint sampler 912 samples, for example, 5 scales, and 6 points for eachscale, for a total of 30 points. For descriptor comparison datageneration for each keypoint, the point sampler 912 samples, forexample, 7 scales, 6 points for each scale, which together with thecenter point results in a total of 43 points.

For each scale, the sample points that are sampled by the point sampler912 are created from a circle having a radius determined based on thescale in which the keypoint is located and the sub-scale value of thekeypoint. In case the sub-scale value is greater or equal to 0, thesample points are sampled from the keypoint scale s where the keypointwas found in to keypoint scale s+4 (for orientation), or to keypointscale s+6 (for comparison). When the sub-scale value is negative, thesample points are sampled from the keypoint scale s to keypoint scales+3 (for orientation), or to keypoint scale s+6 (for comparison). Thesub-scale value refers to an adjustment value that is applied to thekeypoint scale s using sub-pixel refinement to generate a keypoint scalevalue. The keypoint scale value may be different from the integer 1-8keypoint scale s defining the scale that the keypoint was found at. Thekeypoint scale value may be an interpolated non-integer value which canbe any number between 0-9. For example, a keypoint can be found at acertain location for example (x, y)=(100, 200) and at scale 2, aftersub-pixel interpolation the keypoint location can change to (x,y)=(100.3, 199.7) and scale of 2.25.

The point sampler 912 samples the 6 points per scale at a radius r fromthe keypoint that is determined by:

$\begin{matrix}{r = {2*\frac{{\sqrt{2}}^{scale}}{2^{Octave}}}} & {{Equation}2}\end{matrix}$

where Scale starts as the keypoint scale value and is added with 1 forevery subsequent scale, and Octave is the octave of the scale.

In case the keypoint scale value is greater or equal to the keypointscale s, 6 points are sampled from every scale starting with thekeypoint scale s. In case the keypoint scale value is less than thescale s in which the keypoint was found in (e.g., keypoint found inscale 3 with keypoint scale value=2.2), two sets of 6 points are sampledfrom the first scale s, and the rest are sampled from subsequent scales.The formula used to sample all 7 sets of samples for orientationcalculation and comparison generation is shown in Table 1:

TABLE 1 Sample Sample From Scale Sample From Scale Retina Radius WhenKP.Scale < When KP.Scale >= Set Calculation physical scale physicalscale 0 $2*\frac{{\sqrt{2}}^{({Scale})}}{2^{Octave}}$ s s 1$2*\frac{{\sqrt{2}}^{({{Scale} + 1})}}{2^{Octave}}$ s s + 1 2$2*\frac{{\sqrt{2}}^{({{Scale} + 2})}}{2^{Octave}}$ s + 1 s + 2 3$2*\frac{{\sqrt{2}}^{({{Scale} + 3})}}{2^{Octave}}$ s + 2 s + 3 4$2*\frac{{\sqrt{2}}^{({{Scale} + 4})}}{2^{Octave}}$ s + 3 s + 4 5$2*\frac{{\sqrt{2}}^{({{Scale} + 5})}}{2^{Octave}}$ s + 4 s + 5 6$2*\frac{{\sqrt{2}}^{({{Scale} + 6})}}{2^{Octave}}$ s + 5 s + 6

The point sampler 912 alternates between even and odd sampling patternsfor the scales to sample the points around the keypoint at radius r.FIG. 10 shows an even sample pattern 1002 and an odd sample pattern1004, according to one embodiment. For each keypoint, the samplingstarts with the even sample pattern at the keypoint scale s andalternates between the even and odd sample patterns for successivescales. Each of the even sample pattern 1002 and odd sample pattern 1004is a ring with six sample points. The sample points for each pattern arelabeled from 0 to 5, and this order is used for orientation calculationand point comparisons. The x, y locations of each sample point sample_x,sample_y is defined based on the radius r, the orientation angle

, and the use of either the even or odd sample pattern.

For point sampling by the orientation point sampler 912, the orientationangle

used in determining the x,y location of each sample point is set tozero. After the orientation angle

is calculated for a keypoint, the calculated orientation angle

is used in determining the x,y location of each sample point. The centerpoint of the keypoint is also sampled for the comparison sampling. Thepoint sampler 912 reads a patch of image data for each sample point.Each patch may include pixel values used for the orientation anglecalculation and pixel values used for sample point comparisons to avoidre-fetching from the pyramid image buffer 518. The patch size in pixelsmay vary based on the scale.

The intensity value calculator 914 determines an intensity value foreach the sample points using a bilinear interpolation of the neighborpixels at integer grids. For each sample point, the bilinearinterpolation is performed using the patch from the point sampler 912.The patch is used to sample 2×2 pixels around each sample point that isneeded for orientation calculation, then the orientation is applied torotate the sample points thus changing the 2×2 pixels around each samplepoint. As such, the patch may be a 10×10 patch of pixels to ensure thatall the pixels needed for fetching the 2×2 pixels before and afterrotation around each of the sample points are in the patch. The patchsize ensures that all needed pixel values are within the patch so thatno re-fetch from the pyramid image buffer 518 is needed between theorientation calculation and the descriptor generation. The interpolationis performed in the horizontal direction and the vertical directionusing the pixel values of the patch to determine the intensity value ofthe sample point.

The orientation calculator 916 determines the orientation angle

of each keypoint. After the intensity value of each sample point iscalculated, the orientation calculator 916 adds the intensity value intoone of a set of accumulators 922. The orientation calculator 914 mayinclude a total of 6 accumulators 922. Depending on the sample pattern(even vs odd) and label ([0:5]) of the sample points, the intensityvalues go to different accumulators 922. After accumulating all theintensity values, the orientation calculator 916 determines overallorientation values. The orientation calculator 916 determines theorientation angle θ using the overall orientation values.

The orientation calculator 916 provides the orientation angle θ of eachkeypoint to the to the point rotator 918 for rotating the sample pointsby the orientation angle θ.

For each keypoint, the point rotator 918 rotates each of the 43 pointsfrom the point sampler 912 by the orientation angle θ. The 43 samplepoints include the center point at scale s and the remaining 42 pointsthat are sampled from the 7 scales (e.g., s to s+6), with 6 points perscale, by the point sampler 912. The rotation changes the comparisonpoint sampling by rotating the sample points clockwise in the amount ofthe orientation angle.

The intensity value calculator 924 determines an intensity value foreach of the sample points used for comparisons using a bilinearinterpolation of the neighbor (e.g., 2×2) pixels at integer grids. Theintensity value calculator 924 operates like the intensity valuecalculator 914 except that pixel values for rotated sample points areused.

The point comparator 920 performs, for each keypoint, comparisons of theintensity values of pairs of sample points from the intensity valuecalculator 924 to generate descriptor comparison data for each keypoint.For example, the point comparator 920 may perform 472 comparisons foreach keypoint. For a first set of 42 comparisons, the point comparator920 compares the intensity value of the center sample point to theintensity values of the other 42 sample points. For a second set of 105comparisons, the point comparator 920 compares the intensity values ofevery pair of sample points in the same scale. There are 15 comparisonsper ring and a total of 7 rings. For a third set of 216 compares, thepoint comparator 920 compares intensity values of every pair of samplepoints in adjacent scales. There are 36 comparisons per scale pair and 6total adjacent scale pairs. For a fourth set of 108 comparisons, thepoint comparator 920 compares the intensity values of sample points forevery even-ring pair and odd-ring pair. For each ring pair, the samplepoints are compared with those of the same phase in the other scale, andthe sample points with those of the opposite phase in the other scale.Therefore, there are 12 comparison per scale pair and total 9 scalepairs. The number of sample points, the number of comparisons, the orderof the comparisons, or the sample point pairs selected for thecomparisons may vary.

The descriptor comparison data for each keypoint may include a sequenceof bit values, where each of the bit values corresponds with acomparison result between two sample points. For the 472 comparisons,the descriptor comparison data may include 472 bit values with each bitcorresponding to one of the 472 comparisons. For each bit, a value of 1is assigned when a first sample point has a larger intensity value thana second sample point, and a value of 0 is assigned when the firstsample point has a smaller intensity value than the second sample point.

In some embodiments, the sequence of the bit values for a keypointdefines an ordering of the comparison results based on importance levelsof the comparisons. The importance level of a comparison defines howmuch the comparison is representative of features in images. Differentcomparisons between pairs of sample points may have different importancelevels. The point comparator 920 generates bit values defining thecomparison results for each keypoint, where each bit value correspondswith one of the comparison results. The point comparator 920 generatesthe sequence of the bit values defining the ordering of the comparisonresults based on the importance levels of the comparisons.

The sequence of bit values may be ordered with the less significant bitscorresponding with higher importance levels of the comparisons and moresignificant bits corresponding with lower importance levels of thecomparisons. For example, the 472 bit values that represent the 472comparisons may represent the most important comparison with the leastsignificant bit, the second most important comparison with the nextleast significant bit, and so forth with increasing bit significancecorresponding with decreasing importance value for the comparison. Inanother example, less significant bits in the sequence of bit valuescorrespond with lower importance levels of the comparisons and moresignificant bits in the sequence of bit values correspond with higherimportance levels of the comparisons.

The importance level of each comparison may be determined by usingfeature matching. The feature matching criteria may include orientationangle difference of 20 degrees and scale difference within 0.5f. Thelargest match score across all features in a bin is determined for eachfeature comparison pair using:

match score=descriptor length−hamming distance  Equation 3

Matching pairs of features have the largest match score for each other(and least hamming distance). For example, a descriptor for a feature xmay include 10101111110 . . . 011110000 and a matching descriptor for afeature y may include 10111111110 . . . 011110000. As such, the bitimportance for the third bit position that is different is 0 and the bitimportance for other bit positions is 1.

There may also be false positive match pairs generated from featurematching. These false positives may be rejected, such as by usinggeometry verification, inlier track classification, or some othersuitable technique.

A mask may be applied to the outliers based on:

$\begin{matrix}{{{bit}{{importance}\lbrack i\rbrack}} = {\sum\limits_{{({x,y})} \in {pairs}}{{{mask}\lbrack i\rbrack}*{❘{{x.{{descriptor}\lbrack i\rbrack}} - {y.{{descriptor}\lbrack i\rbrack}}}❘}}}} & {{Equation}4}\end{matrix}$

where bit importance is the importance level, i is an index ofcomparisons, x and y are matching features, and mask is defined by:

$\begin{matrix}{{{mask}\lbrack i\rbrack} = \{ {\begin{matrix}1 \\0\end{matrix}\begin{matrix}({inlier}) \\({outlier})\end{matrix}} } & {{Equation}5}\end{matrix}$

The importance levels of the comparisons may be determined using adataset (e.g., of 384 sequences) that is selected to include differentscenarios, such as different motion and lighting conditions. Each of thecomparisons is assigned to a bit of the sequence of bit values based onthe importance levels to generate the sequence of bit values.

In some embodiments, bits corresponding with one or more of the leastimportant comparisons may be removed or excluded from the sequence ofbit values to reduce the data size of the keypoint descriptor. The pointcomparator 920 may operate in different modes, each mode using adifferent number of bit values for the sequence of bit values. Forexample, a first mode may use 471 bits, a second mode may use 256 bits,and a third mode may use 128 bits. By ordering the sequence of bitvalues based on the importance level of the comparisons, bitscorresponding with the least important comparisons may be removed fromone end of the sequence of bit values (e.g., the least or mostsignificant bits), resulting in the remaining bit values representingthe most important comparisons. As such, the effectiveness of thekeypoint descriptor in terms of being representative of features isincreased for any chosen bit length. For example, if the sequence of bitvalues is selected to be 256 bits, then 215 bits representing the leastimportant 215 comparisons are removed or excluded from the full 471 bitsequence to leave the 256 bit values. The number of modes and the numberof bit values in the sequence for each mode may vary.

In some embodiments, the comparisons performed by the point comparator920 may vary based on the mode of operation. The point comparator 920may skip performing the comparisons that correspond with bits that willbe removed or excluded from the sequence. For example, if the sequenceof bit values is selected to be 256 bits, then the point comparator 920may perform only the 256 most important comparisons. Reducing the numberof comparisons may increase the speed of keypoint descriptor generationand reduce use of computing resources (e.g., in terms of processing andmemory).

The point comparator 920 combines the descriptor comparison dataincluding the sequence of bit values of each keypoint with the keypointparameters of the keypoint to generate the full keypoint descriptor. Thepoint comparator 920 receives the keypoint parameters and keypoints fromthe keypoint list 528 for combination with the descriptor comparisondata. The point comparator 920 then outputs the keypoint descriptor.

The descriptor generator controller 926 controls the mode of operationof the point comparator 920. The descriptor generator controller 926 mayreceive instructions 930 from the CPU 208 regarding the mode ofoperation and control the point comparator 920 according to theinstructions 930. The selection of the mode of operation may be based onconsideration of a tradeoff between the sufficient level of accuracy(e.g., the longer the keypoint descriptor, the more accurate it is) andthe memory footprint of the list of keypoint descriptors (e.g., theshorter the descriptor, the smaller the memory footprint. Based on theinstructions, the descriptor generator controller 926 configures thepoint comparator 920 to operate under one or more of the modes ofoperation to generate the descriptor comparison data.

In some embodiments, the keypoint descriptor generator 530 may operatein multiple modes of operation. The keypoint descriptor generator 530may generate and multiple sets of keypoint descriptors, each setincluding a different number of bit values in the sequence of bitvalues. For example a long version of a keypoint descriptor may beoutput to the system memory 230 (e.g., where memory capacity is not anissue) and can be used for applications requiring very accuratedescriptor type, while a short version of the keypoint descriptor may bewritten into a more tightly coupled memory (e.g., where the amount ofmemory is limited) and is used for applications that don't need the highaccuracy and are more sensitive to latency and thus operate in closetightly coupled memory.

Example Processes for Keypoint Descriptor Generation and KeypointDetermination

FIG. 11 is a flowchart illustrating a method for keypoint descriptorgeneration, according to one embodiment. The method may includeadditional or fewer steps, and steps may be performed in differentorders. The keypoint descriptor generator 530 of the feature extractorcircuit 500 generates descriptor comparator data of a keypointdescriptor for each keypoint. The descriptor comparator data for akeypoint includes a sequence of the bit values defining an ordering ofcomparison results based on importance levels of the comparisons. Theordering of the sequence of bit values facilitates the keypointdescriptor generator 530 being configurable to operate under multiplemodes of operation. The descriptor comparison data has different bitlengths under the different modes of operation via removal or exclusionof bits from the sequence for lower importance comparisons.

The keypoint descriptor generator 530 determines 1110 intensity valuesof sample points in pyramid images of an image pyramid for a keypoint.For example, a pyramid image generator generates an image pyramid froman input image that includes pyramid images at different octaves andscales. The keypoint descriptor generator 530 samples a patch of imagedata from a pyramid image for each sample point of the keypoint. Thekeypoint descriptor generator 530 determines an orientation angle of thekeypoint and rotates the sample points by the orientation angle. Thekeypoint descriptor generator 530 determines the intensity values of thesample points using the rotated sample points, such as by performing aninterpolation of a group of pixel values in the patch of each samplepoint. The sample points of the keypoint include a center point at ascale of the image pyramid, sample points around the center point at thescale, and sample points at other scales of the image pyramid.

The keypoint descriptor generator 530 determines 1120 comparison resultsof comparisons between the intensity values of pairs of the samplepoints. The comparisons may include comparisons between the center pointto other (e.g., 42) sample points, comparisons between pairs of samplepoints in the same scale, comparisons between pairs of sample points inadjacent scales, and comparisons between pairs of sample points foreven-ring pairs and odd-ring pairs.

The keypoint descriptor generator 530 generates 1130 bit values definingthe comparison results for the keypoint, each bit value correspondingwith one of the comparison results. For each bit, a value of 1 isassigned when a first sample point has a larger intensity value than asecond sample point, and a value of 0 is assigned when the first samplepoint has a smaller intensity value than the second sample point.

The keypoint descriptor generator 530 generates 1140 a sequence of thebit values defining an ordering of the comparison results based onimportance levels of the comparisons. The importance level of eachcomparison defines how much the comparison is representative offeatures.

The sequence of bit values may exclude a portion of the bit valuescorresponding with comparisons having lower or the lowest importancelevels. In one example, there are 472 comparisons performed to generate472 comparison results while the sequence of bit values may include 256bits representing the 256 most important comparison results or 128 bitsrepresenting the 128 most important comparison results. The keypointdescriptor generator 530 may have using different modes of operationthat correspond with different bit lengths from the sequence of bitvalues. In some embodiments, the keypoint descriptor generator 530 mayuse multiple modes of operation to generate keypoint descriptors ofdifferent bit lengths for each keypoint.

Bit values corresponding with the least important comparisons may beremoved from the beginning or the end of the sequence of bit values tosatisfy the specified bit length, depending on how the comparisons areordered in the sequence of bit values. The less significant bits in thesequence of bit values may correspond with higher importance levels ofthe comparisons and the more significant bits in the sequence of bitvalues may correspond with lower importance levels of the comparisons.The keypoint descriptor generator 530 may update the sequence of bitvalues by removing one or more most significant bits from the sequenceof the bit values as specified by the bit length. Alternatively, theless significant bits in the sequence of bit values may correspond withlower importance levels of the comparisons and the more significant bitsin the sequence of bit values may correspond with higher importancelevels of the comparisons. The keypoint descriptor generator 530 mayupdate the sequence of bit values by removing one or more most leastbits from the sequence of the bit values as specified by the bit length.

The keypoint descriptor generator 530 may combine the sequence of bitvalues with keypoint parameters of the keypoint including the x and ylocations, the keypoint scale value, and the Laplacian score to generatethe full keypoint descriptor. The keypoint descriptor generator 530 mayprovide the keypoint descriptors to the system memory 230 for sharingwith the CPU 208, or to another component of an image processingpipeline. The method of FIG. 11 may be used for each keypoint of theimage pyramid to generate the keypoint descriptor.

While particular embodiments and applications have been illustrated anddescribed, it is to be understood that the invention is not limited tothe precise construction and components disclosed herein and thatvarious modifications, changes and variations which will be apparent tothose skilled in the art may be made in the arrangement, operation anddetails of the method and apparatus disclosed herein without departingfrom the spirit and scope of the present disclosure.

What is claimed is:
 1. An apparatus, comprising: a pyramid imagegenerator circuit configured to generate an image pyramid from an inputimage, the image pyramid including pyramid images at different octavesand scales; and a keypoint descriptor generator circuit coupled to thepyramid image generator circuit, the keypoint descriptor generatorcircuit configured to: compare intensity values of pairs of samplepoints for a keypoint; generate, from intensity comparison results, abit descriptor for the keypoint, the bit descriptor comprisinginformation of importance levels of the sample points; and remove one ormore bits in the bit descriptor that correspond to the sample pointsthat have the importance levels lower than the sample points remained inthe bit descriptor.
 2. The apparatus of claim 1, wherein the importancelevel of a sample point defining how much the sample point isrepresentative of features.
 3. The apparatus of claim 1, wherein lesssignificant bits in the bit descriptor correspond with higher importancelevels and more significant bits in the bit descriptor correspond withlower importance levels.
 4. The apparatus of claim 3, wherein thekeypoint descriptor generator circuit is further configured to updatethe bit descriptor by removing one or more most significant bits fromthe bit descriptor.
 5. The apparatus of claim 1, wherein lesssignificant bits in the bit descriptor correspond with lower importancelevels and more significant bits in the bit descriptor correspond withhigher importance levels.
 6. The apparatus of claim 5, wherein thekeypoint descriptor generator circuit is further configured to updatethe bit descriptor by removing one or more least bits from the bitdescriptor.
 7. The apparatus of claim 1, wherein the keypoint descriptorgenerator circuit is configured to: sample a patch of image data from apyramid image for each sample point of the keypoint; and determine theintensity value of the sample point using the patch.
 8. The apparatus ofclaim 1, wherein: the keypoint descriptor generator circuit is furtherconfigured to: determine an orientation angle of the keypoint; androtate the sample points by the orientation angle, wherein the intensityvalues of the sample points are determined using the rotated samplepoints.
 9. The apparatus of claim 1, wherein the sample points for thekeypoint include a center point at a scale of the image pyramid, a firstplurality of sample points around the center point at the scale, and asecond plurality of sample points at each of a plurality of other scalesof the image pyramid.
 10. The apparatus of claim 1, wherein the keypointdescriptor generator circuit is further configured to, for each samplepoint, perform an interpolation of a group of pixels values around acenter point of the sample point.
 11. The apparatus of claim 1, furthercomprising a pyramid image buffer coupled to the pyramid image generatorcircuit and the keypoint descriptor generator circuit, the pyramid imagebuffer configured to store the pyramid images received from the pyramidimage generator circuit and provide the pyramid images to the keypointdescriptor generator circuit.
 12. The apparatus of claim 1, wherein thebit descriptor is a first bit descriptor, and the keypoint descriptorgenerator circuit is further configured to: generate a second bitdescriptor for the keypoint, the first bit descriptor and the second bitdescriptor having different bit lengths; and output the first bitdescriptor and the second bit descriptor.
 13. A method comprising:generating an image pyramid from an input image, the image pyramidincluding pyramid images at different octaves and scale; comparingintensity values of pairs of sample points for a keypoint; generating,from intensity comparison results, a bit descriptor for the keypoint,the bit descriptor comprising information of importance levels of thesample points; and removing one or more bits in the bit descriptor thatcorrespond to the sample points that have the importance levels lowerthan the sample points remained in the bit descriptor.
 14. The method ofclaim 13, wherein the importance level of a sample point defining howmuch the sample point is representative of features.
 15. The method ofclaim 13, further comprising: sampling a patch of image data from apyramid image for each sample point of the keypoint; and determining theintensity value of the sample point using the patch.
 16. The method ofclaim 13, further comprising: determining an orientation angle of thekeypoint; and rotating the sample points by the orientation angle,wherein the intensity values of the sample points are determined usingthe rotated sample points.
 17. A system comprising: an image sensorconfigured to obtain an input image; and an image signal processorcoupled to the image sensor, the image signal processor configured to:generate an image pyramid from the input image, the image pyramidincluding pyramid images at different octaves and scale; compareintensity values of pairs of sample points for a keypoint; generate,from intensity comparison results, a bit descriptor for the keypoint,the bit descriptor comprising information of importance levels of thesample points; and remove one or more bits in the bit descriptor thatcorrespond to the sample points that have the importance levels lowerthan the sample points remained in the bit descriptor.
 18. The system ofclaim 17, wherein the importance level of a sample point defining howmuch the sample point is representative of features.
 19. The system ofclaim 17, wherein the image signal processor further configured to:sample a patch of image data from a pyramid image for each sample pointof the keypoint; and determine the intensity value of the sample pointusing the patch.
 20. The system of claim 17, where the image signalprocessor further configured to: determine an orientation angle of thekeypoint; and rotate the sample points by the orientation angle, whereinthe intensity values of the sample points are determined using therotated sample points.